perioperative information management systems: driving discovery & reliability in the operating...
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Perioperative Information Management Systems: Driving Discovery & Reliability In The Operating Room. Jesse M. Ehrenfeld, M.D., M.P.H. Assistant Professor of Anesthesiology Assistant Professor of Biomedical Informatics Director, Perioperative Data Systems Research - PowerPoint PPT PresentationTRANSCRIPT
Perioperative Information Management Systems: Driving Discovery & Reliability
In The Operating Room
Jesse M. Ehrenfeld, M.D., M.P.H. Assistant Professor of Anesthesiology
Assistant Professor of Biomedical InformaticsDirector, Perioperative Data Systems Research
Director, Center for Evidence-Based Anesthesia Medical Director, Perioperative Quality
Co-Director, Vanderbilt Program for LGBTI Health
Vanderbilt University School of MedicineDepartment of Anesthesiology
Overview
Part I – Perioperative Information Management SystemsOverview & FunctionalityReliable Processes
Part II – Clinical Decision SupportThe Problem, The Need, Opportunities
Part III – Our Research & The FutureUsing PIMS to Measure & Increase ReliabilityPredictive Modeling / Real Time Feedback LoopsCase Studies: Blood Pressure Gaps & Glucose Control
Vanderbilt Department of Anesthesiology
60,000 adult and pediatric patient encounters
90 anesthetizing locations
20,000 patients are seen in the Vanderbilt Preoperative Evaluation Clinic (VPEC)
3,000 patients are seen annually in our Vanderbilt Interventional Pain Center
20,000 Vanderbilt adult and pediatric patients receive an anesthetic during a radiologic, gastrointestinal, or other diagnostic or therapeutic procedure
Provide care in eight intensive care units, including six adult, the pediatric and neonatal intensive care units
4,000 anesthetics per year in the labor and delivery suite
Perioperative Data Systems Research Group
DirectorJesse Ehrenfeld, MD
Data Intelligence Analyst
Jason Denton
Health Systems Database Analyst
Chris Eldridge
Data Warehouse Architect
Michealene Johnson
Health Systems Database Analyst
Dylan Snyder
Research AssistantRasheeda Lawson
Research Analyst
Khensani Marolen
Data Management
SpecialistTBD
Project Manager
Angelo del Puerto
Last updated 7.2012
Graduate Students• Amlan Bhattacharjee• Sean Chester• Kristen Eckstrand• Aneesh Goel• Paul Hannam• Mary Marschner• Monika Jering• Ilana Stohl
Undergraduate Students• Molly Cowan• Lindsay Lee• Shane Selig• Jacob Shiftan• Emily Wang
Overview
Part I – Perioperative Information Management SystemsOverview & FunctionalityReliable Processes
Part II – Clinical Decision SupportThe Problem, The Need, Opportunities
Part III – Our Research & The FutureUsing PIMS to Measure & Increase ReliabilityPredictive Modeling / Real Time Feedback LoopsCase Studies: Blood Pressure Gaps & Glucose Control
Biomedical Informatics
Medical Informatics• Intersection of information
science, computer science and health care
• Resources, devices, methods optimize information acquisition, storage, retrieval and use
• Involves computers, clinical guidelines, information, medical terminologies, communications systems
Perioperative Information Management Systems
Accurate / reliable data recording
Interface with hospital-wide EHR
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Size
LowHigh
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Population/Development
RuralUrban
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Academic Status
Teaching
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GeographicalDistribution
NortheastSoutheastSouthwestMidwestWest
PIMS Adoption in the U.S. – 2011
Stohl, Sandberg, Ehrenfeld. Assoc. of SCIP Compliance with Use of a PIMS. (submitted)
Small
Large
Areas Impacted by PIMS
Major Areas of Impact
PatientsDepartment
al managemen
t
Clinical Practice
Ehrenfeld, J.M., Rehman, M.A. “Anesthesia Information Management Systems: Current Functionality and Limitations” (2010) Journal of Clinical Monitoring and Computing Aug 24
PIMS: Impact on Patients
• Provision of real-time intraoperative decision support
• Allows the anesthesia care team to focus on the patient, rather than recording vital signs
• Better legibility and availability of historical records
• More precise recording of intraoperative data & patient responses to anesthesia
Impact on patients
Chau, A., Ehrenfeld, J.M. “Using Real Time Clinical Decision Support to Improve Performance on Perioperative Quality and Process Measures” (2011) Anesthesiology Clinics
PIMS: Impact on Dept Management
• Supply cost analysis by provider/type of surgery/patient
• Improved billing accuracy and timeliness
• Fulfills the Joint Commission requirements for legible and comprehensive patient records
• Facilitates verification of Accreditation Council for Graduate Medical Education case requirements for trainees
• Simplifies compliance with concurrency and other regulatory issues
Impact on Department
al Manageme
nt
Chau, A., Ehrenfeld, J.M. “Using Real Time Clinical Decision Support to Improve Performance on Perioperative Quality and Process Measures” (2011) Anesthesiology Clinics
PIMS: Impact on Clinical Practice
• Provides precise, high-resolution records which can be used for educational purposes
• Enables researchers to rapidly find rare events or specific occurrences across a large number of cases
• Facilitates individual provider performance tracking
• Allows better quality assurance functionality through the creation of more complete and precise records
• Integration with other hospital databases can allow assessment of short and long term patient outcomes
• Provision of additional legal protection via the availability of unbiased, precise information
Impact on Clinical Practice
Chau, A., Ehrenfeld, J.M. “Using Real Time Clinical Decision Support to Improve Performance on Perioperative Quality and Process Measures” (2011) Anesthesiology Clinics
13Mobile PIMS: VigiVUTM
Transformative technology
•Enhance situational awareness•Enable development of new anesthesia care models•Significant impact on operational efficiency
14Mobile PIMS: VigiVUTM
Push Notifications
Push Notifications• Abnormal vital signs• Lab results• Operational
notifications• Patient in holding• Patient in OR• Surgeon closing
• Notable drug events• Vasoactives
Process Reliability
Processes are collections of systems and actions following prescribed procedures for bringing about a result.
Reliability of any processes can be determined using data when process failure criteria are established.
Results of the analysis can be graphically displayed, problems identified, categorized and identified for corrective action.
The hardest part of any reliability analysis is getting the data.
Process Reliability in Health Care
Given our intentions, as talented providers, why are clinical processes carried out at such low levels of reliability?
Don’t show up for work wanting to provide bad care!
‘‘It’s the system, not the people’’ – true, but not helpful as we aim to improve our processes
Resar, RK. Making Noncatastrophic Health Care Processes Reliable. Health Serv Res. 2006.
Process Reliability in Health Care
Reasons for reliability gap:
Health care improvement methods excessively dependent on vigilance and hard work
We benchmarking to mediocre outcomes in health care – leads to false sense of process reliability
Allow clinical autonomy creates wide, unjustifiable, performance variation
Processes not designed to meet specific, articulated reliability goals.
Resar, RK. Making Noncatastrophic Health Care Processes Reliable. Health Serv Res. 2006.
Overview
Part I – Perioperative Information Management SystemsOverview & FunctionalityReliable Processes
Part II – Clinical Decision SupportThe Problem, The Need, Opportunities
Part III – Our Research & The FutureUsing PIMS to Measure & Increase ReliabilityPredictive Modeling / Real Time Feedback LoopsCase Studies: Blood Pressure Gaps & Glucose Control
Clinical Decision Support
Perioperative Info. Management Systems• Not just record
keeping systems• Facilitate application of
• collective wisdom of previous
• cases to your current patient
• “Big brain” in the sky• Advice and support
Problem/Need
Why do we need clinical decision support?
Mistakes happen You own a calculator don’t you?
Knowledge evolves Pubmed / Medline
Problem/Need
To err is human•Time constraints•Frequent interruptions•Limits of memory•Multi-tasking•Fatigue
Not just looking for errors
•Define optimal care improve our performance
General Solution: Decision Support
“Clinical consultation systems that use population statistics and expert knowledge to offer real-time advice to clinicians…they provide for patient specific information management and consultation.”
- EH Shortliffe, JAMA 1987;258:61-6
Clinical Decision SupportObjective: assist clinicians in (1) making the best clinical decision and (2) following recommended practices
Wide range of tools: very simple data field checks complex calculations performed in the background
Potential to changes approaches to patient safety Reactive Proactive
General Solution: Decision Support
Goals in the Operating Room: Optimize outcomes by enabling physicians Reduce errors by providing reminders Increase skill by sharing information
Data
Data
Data
Data
OR Decision Support Hierarchy
Type Consequence Level
Level of Difficulty
Managerial Low Low Example: Bayesian analysis to predict amount of surgical time remainingProcess of Care Medium Medium
Example: SCIP measures (antibiotics before incision, normothermia, etc.)Outcome Based High High
Example: Provide risk-adjusted 30 day post-op pain scores after arthoplasty
OR Decision Support Hierarchy
Type Consequence Level
Level of Difficulty
Managerial Low Low Example: Bayesian analysis to predict amount of surgical time remainingProcess of Care Medium Medium
Example: SCIP measures (antibiotics before incision, normothermia, etc.)Outcome Based High High
Example: Provide risk-adjusted 30 day post-op pain scores after arthoplasty
OR Decision Support Hierarchy
Type Consequence Level
Level of Difficulty
Managerial Low Low Example: Bayesian analysis to predict amount of surgical time remainingProcess of Care Medium Medium
Example: SCIP measures (antibiotics before incision, normothermia, etc.)Outcome Based High High
Example: Provide risk-adjusted 30 day post-op pain scores after arthoplasty
OR Decision Support Hierarchy
Type Consequence Level
Level of Difficulty
Managerial Low Low Example: Bayesian analysis to predict amount of surgical time remainingProcess of Care Medium Medium
Example: SCIP measures (antibiotics before incision, normothermia, etc.)Outcome Based High High
Example: Provide risk-adjusted 30 day post-op pain scores after arthoplasty
Clinical Decision Support
I’m not convinced. Does it really make a difference?
Perioperative Information Management Systems (PIMS) Mediate Improved SCIP Compliance Compared to Hospitals Without PIMS
Stohl, Sandberg, Ehrenfeld. Assoc. of SCIP Compliance with Use of an PIMS. (submitted)
Decision Support Version 1.0
Outside the Operating Room Web-based tools Computerized Physician Order
Entry PDA, iPhone applications
Inside the Operating Room Anesthesia Information
Management Systems
Clinical Decision Support 2.0
Machine Learning
Techniques
Artificial Intelligence
Advanced Algorithms
Contextual InformationProcessing
PreviousCases
ClinicalGuidlines
Real-TimeData
Clinical Decision Support 2.0
SURGICAL EVENT(blood loss, allergy, etc)
orEXTERNAL EVENT
(lab values, new info, etc)
SUGGESTIONS / GUIDELINES /
STATISTICS
IDEAL RESPONSE
DATA FROM ALL PREVIOUS CASES
Clinical Decision Support 2.0
Envelop of Care
Case Progression Over Time
Clinical Decision Support 2.0
Envelop of Care
Case Progression Over Time
Clinical Decision Support 2.0
Envelop of Care
Case Progression Over Time
Clinical Decision Support 2.0
Envelop of Care
Case Progression Over Time
Alert
Clinical Decision Support 2.0
Envelop of Care
Case Progression Over Time
Alert
Clinical Decision Support 2.0
Envelop of Care
Case Progression Over Time
Alert
Alerting
Once you generate knowledge/ information, how do you disseminate it?
Alerting modalities: Who and How? Identify appropriate provider Get their attention:
On-screen pop-ups Pager messages Emails
Limitations/Factors
Usability: Ability to provide a useful function.
Does it do anything of value?
Limitations/Factors
Ergonomics: The study of how people interact with their
environment. Can physicians use it?
Limitations/Factors
Latency: Delays in usage and availability.
Will it work in a time-sensitive scenario?
Limitations/Factors
Interconnectivity / Interoperability: Ability to connect to other sources of information and
share information effectively. Does it network well with existing infrastructure?
Limitations/Factors
Ability to Adapt: If we don’t have the knowledge, can the system be
used to generate missing info? Can it develop a hypothesis?
Summary: Process Monitoring & Control
Goal: right inforight time
right person
Keys to electronic process monitoring Process models Process exceptions Alert Generation
Overview
Part I – Perioperative Information Management SystemsOverview & FunctionalityReliable Processes
Part II – Clinical Decision SupportThe Problem, The Need, Opportunities
Part III – Our Research & The FutureUsing PIMS to Measure & Increase ReliabilityPredictive Modeling / Real Time Feedback LoopsCase Studies: Blood Pressure Gaps & Glucose Control
Required Components
Define Norms of Practice / Baseline
Real-Time Data Capture
Alerting Mechanism
Measure Outcomes
Required Components
Define Norms of Practice / Baseline
Real-Time Data Capture
Alerting Mechanism
Measure Outcomes Increasing D
ifficulty
Required Components
Define Norms of Practice / Baseline
Real-Time Data Capture
Alerting Mechanism
Measure Outcomes Increasing D
ifficulty
Decision Support Engine
Define Norms of Practice
Single center retrospective analysis of PIMS data Equipment performance characteristics
Ehrenfeld, J.M., Walsh, J.L. & Sandberg, W.S. “Right and Left Sided Mallinckrodt Double Lumen Tubes Have Identical Clinical Performance” Anesthesia & Analgesia. (2008) 106 (6) 1847-1852.
Physiologic Monitoring Ehrenfeld, J.M., Epstein, R.H., Bader, S., Kheterpal, S., Sandberg, W.S. “Automatic Notifications
Mediated by Anesthesia Information Management Systems Reduce the Frequency of Prolonged Gaps in Blood Pressure Documentation” Anesthesia & Analgesia. (2011) Aug;113(2):356-63. Epub 2011 Mar 17.
Ehrenfeld, J.M., Funk, L.M, Van Schalkwyk, J., Merry, A., Sandberg, W.S., Gawande, A. “Incidence of Hypoxemia During Surgery: Evidence from Two Institutions” Canadian Journal of Anesthesia. 2010: 57 (10) 888-97.
Predictors of Blood Transfusion Henneman, J.P., Ehrenfeld, J.M. “A Predictive Model For Intraoperative Blood Product Requirements”
IARS, 5/11 Multi-center data aggregation (MPOG)
Epidural abscess / hematoma Bateman, B.T., Mhyre, J.M., Ehrenfeld, J.M., Kheterpal, Abbey, K.R., Argalious, M., Berman, M.F., St. Jacques, P., Levy, W., Loeb, R.G., Paganelli, W., Smith, K.W., Wethington, K.L., Wax, D., Pace, N.L., Tremper, K., Sandberg, W.S. “The Risk and Outcomes of Epidural Hematomas and Abscesses Following Perioperative and Obstetric Epidural Catheterization: A Report from the MPOG Research Consortium.” Anesth Analg. 2012 Apr 13.
Alerting Mechanisms
Notification modalities Pagers / iPhones On-screen pop-ups Vibration belts Heads-up displays
Frequency One time vs. Multiple
Level of Acknowledgment Hard-Stop vs. Soft Alerts
Alerts to Drive Performance
Assessments of Cognitive Deficits in Mutant MiceRamona Marie Rodriguiz and William C. Wetsel Duke University Medical Center
Active Avoidance Learning
Outcomes Measurement
What are the Outcomes Process of Care
“Wake-Up” time / Time to extubation Room turnover time Time to discharge from PACU
Patient Centered Post-operative pain scores (immediate, 30 days) Rates of PONV and PDNV 30 day re-admission rates Mortality, wound infection rates
1. GAPS IN BLOOD PRESSURE MONITORING
2. INTRAOPERATIVE GLUCOSE MONITORING
3. REAL TIME PATIENT PREDICTIVE MODELS
4. ENHANCING VALUE IN ANESTHESIA
A Few Quick Examples … …To Bring It All Together
GAPS IN BLOOD PRESSURE MONITORING
Example #1
Ehrenfeld J, Epstein RH, Bader S, Kheterpal S, Sandberg WS. Automatic notifications mediated by anesthesia information management systems reduce the frequency of prolonged gaps in blood pressure documentation. Anesth Analg 2011;113:356–63
Gaps in Physiologic Monitoring
BP reading: 9:52 amInduction: 9:53 amBP reading: 10:08 am (16 minutes later)
Blood Pressure Gaps: Results
Blood Pressure Gaps: Results
INTRAOPERATIVE GLUCOSE MONITORING
Example #2
Closing Example
Diabetes Management
12.22%
24.33%
38.21%
57.84%63.90%
77.52%80.70%
87.88%
100.00% 100.00%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 >9 hrsSurgical Duration
(excludes anesthesia induction & emergence time)
Diabetes Patients Receiving Intraoperative Insulin Who Had Intraoperative Glucose Measured
Peterfreund, R.P., McCartney, K., Ehrenfeld, J.M. “Impact of Intraoperative Glucose Notifications” ASA 2012 (accepted)
Diabetes Management
12.22%
24.33%
38.21%
57.84%63.90%
77.52%80.70%
87.88%
100.00% 100.00%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
70.00%
80.00%
90.00%
100.00%
0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 >9 hrsSurgical Duration
(excludes anesthesia induction & emergence time)
Diabetes Patients Receiving Intraoperative Insulin Who Had Intraoperative Glucose Measured
Peterfreund, R.P., McCartney, K., Ehrenfeld, J.M. “Impact of Intraoperative Glucose Notifications” ASA 2012 (accepted)
Better Care for Diabetic Patients
Peterfreund, R.P., McCartney, K., Ehrenfeld, J.M. “Impact of Intraoperative Glucose Notifications” ASA 2012 (accepted)
Better Care for Diabetic Patients
Reduced Readmission
Rates
Peterfreund, R.P., McCartney, K., Ehrenfeld, J.M. “Impact of Intraoperative Glucose Notifications” ASA 2012 (accepted)
REAL TIME PREDICTIVE PATIENT MODELS
Example #3
“Enhancing Perioperative
Safety Through the Determination of Intraoperative
Predictors of Post-Operative
Deterioration”
Funded by Anesthesia Patient Safety Foundation
PI – J. Ehrenfeld
ENHANCING VALUE IN ANESTHESIA
Example #4
Enhancing Value in Anesthesia
Wanderer, J.P., Hester, D., Ehrenfeld, J.M. “Cost Variability in Anesthesia Services” ASA 2012 (accepted)
Enhancing Value in Anesthesia
Wanderer, J.P., Hester, D., Ehrenfeld, J.M. “Cost Variability in Anesthesia Services” ASA 2012 (accepted)
Enhancing Value in Anesthesia
Value Cost
Enhancing Value in Anesthesia
Value
Cost
Quality
Enhancing Value in Anesthesia
Wanderer, J.P., Hester, D., Ehrenfeld, J.M. “Cost Variability in Anesthesia Services” ASA 2012 (accepted)
Vanderbilt Anesthesia Optimal Care Score
Real-Time Perioperative Dashboard
Blood Product Utilization Dashboard
Overview
Part I – Perioperative Information Management SystemsOverview & FunctionalityReliable Processes
Part II – Clinical Decision SupportThe Problem, The Need, Opportunities
Part III – Our Research & The FutureUsing PIMS to Measure & Increase ReliabilityPredictive Modeling / Real Time Feedback LoopsCase Studies: Blood Pressure Gaps & Glucose Control
What Does the Future Hold
More “Decision Support 2.0” Live comparison of current clinical data Indexed (pre-sorted) set of cases
Matching closest cases on surgery, age, ASA, etc
More Outcomes Beyond PONV & the SSN death index
More Notification Modalities & Mobile Apps
More Patient Specific Real-Time Prediction Models
Perioperative Genomics
Conclusions
Medical Informatics will empoweranesthesiologists in the 21st century
Vanderbilt Perioperative Data Systems Research Group